{"product_id":"cognitive-analytics-and-reinforcement-learning-theories-techniques-and-applications-hardback-9781394214037","title":"Cognitive Analytics and Reinforcement Learning; Theories, Techniques and Applications (Hardback) 9781394214037","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eCognitive Analytics and Reinforcement Learning\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eTheories, Techniques and Applications\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eElakkiya R. (Edited by), Elakkiya (Author), Subramaniyaswamy V. (Edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781394214037, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 19 April 2024\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e384 pages\u003cbr\u003e22.9 x 15.2 x 2.4 cm, 0.844 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cb\u003eCOGNITIVE ANALYTICS AND REINFORCEMENT LEARNING\u003c\/b\u003e \u003cp\u003e\u003cb\u003eThe combination of cognitive analytics and reinforcement learning is a transformational force in the field of modern technological breakthroughs, reshaping the decision-making, problem-solving, and innovation landscape; this book offers an examination of the profound overlap between these two fields and illuminates its significant consequences for business, academia, and research.\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003eCognitive analytics and reinforcement learning are pivotal branches of artificial intelligence. They have garnered increased attention in the research field and industry domain on how humans perceive, interpret, and respond to information. Cognitive science allows us to understand data, mimic human cognitive processes, and make informed decisions to identify patterns and adapt to dynamic situations. The process enhances the capabilities of various applications.  \u003c\/p\u003e\n\u003cp\u003eReaders will uncover the latest advancements in AI and machine learning, gaining valuable insights into how these technologies are revolutionizing various industries, including transforming healthcare by enabling smarter diagnosis and treatment decisions, enhancing the efficiency of smart cities through dynamic decision control, optimizing debt collection strategies, predicting optimal moves in complex scenarios like chess, and much more. With a focus on bridging the gap between theory and practice, this book serves as an invaluable resource for researchers and industry professionals seeking to leverage cognitive analytics and reinforcement learning to drive innovation and solve complex problems. \u003c\/p\u003e\n\u003cp\u003eThe book’s real strength lies in bridging the gap between theoretical knowledge and practical implementation. It offers a rich tapestry of use cases and examples. Whether you are a student looking to gain a deeper understanding of these cutting-edge technologies, an AI practitioner seeking innovative solutions for your projects, or an industry leader interested in the strategic applications of AI, this book offers a treasure trove of insights and knowledge to help you navigate the complex and exciting world of cognitive analytics and reinforcement learning. \u003c\/p\u003e\n\u003cp\u003e\u003cb\u003eAudience\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003eThe book caters to a diverse audience that spans academic researchers, AI practitioners, data scientists, industry leaders, tech enthusiasts, and educators who associate with artificial intelligence, data analytics, and cognitive sciences.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003ePreface xiii\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart I: Cognitive Analytics in Continual Learning 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Cognitive Analytics in Continual Learning: A New Frontier in Machine Learning Research 3\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eRenuga Devi T., Muthukumar K., Sujatha M. and Ezhilarasie R.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 4\u003c\/p\u003e \u003cp\u003e1.2 Evolution of Data Analytics 5\u003c\/p\u003e \u003cp\u003e1.3 Conceptual View of Cognitive Systems 7\u003c\/p\u003e \u003cp\u003e1.4 Elements of Cognitive Systems 7\u003c\/p\u003e \u003cp\u003e1.5 Features, Scope, and Characteristics of Cognitive System 9\u003c\/p\u003e \u003cp\u003e1.6 Cognitive System Design Principles 12\u003c\/p\u003e \u003cp\u003e1.7 Backbone of Cognitive System Learning\/Building Process 13\u003c\/p\u003e \u003cp\u003e1.8 Cognitive Systems vs. AI 17\u003c\/p\u003e \u003cp\u003e1.9 Use Cases 18\u003c\/p\u003e \u003cp\u003e1.10 Conclusion 25\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Cognitive Computing System-Based Dynamic Decision Control for Smart City Using Reinforcement Learning Model 29\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eSasikumar A., Logesh Ravi, Malathi Devarajan, Hossam Kotb and Subramaniyaswamy V.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 30\u003c\/p\u003e \u003cp\u003e2.2 Smart City Applications 32\u003c\/p\u003e \u003cp\u003e2.3 Related Work 36\u003c\/p\u003e \u003cp\u003e2.4 Proposed Cognitive Computing RL Model 39\u003c\/p\u003e \u003cp\u003e2.5 Simulation Results 45\u003c\/p\u003e \u003cp\u003e2.6 Conclusion 47\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Deep Recommender System for Optimizing Debt Collection Using Reinforcement Learning 51\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eKeerthana S., Elakkiya R. and Santhi B.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 52\u003c\/p\u003e \u003cp\u003e3.2 Terminologies in RL 54\u003c\/p\u003e \u003cp\u003e3.3 Different Forms of RL 57\u003c\/p\u003e \u003cp\u003e3.4 Related Works 59\u003c\/p\u003e \u003cp\u003e3.5 Proposed Methodology 62\u003c\/p\u003e \u003cp\u003e3.6 Result Analysis 66\u003c\/p\u003e \u003cp\u003e3.7 Conclusion 68\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II: Computational Intelligence of Reinforcement Learning 73\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Predicting Optimal Moves in Chess Board Using Artificial Intelligence 75\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eThangaramya K., Logeswari G., Sudhakaran G., Aadharsh R., Bhuvaneshwar S., Dheepakraaj R. and Parasu Sunny\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 76\u003c\/p\u003e \u003cp\u003e4.2 Literature Survey 83\u003c\/p\u003e \u003cp\u003e4.3 Proposed System 88\u003c\/p\u003e \u003cp\u003e4.4 Results and Discussion 95\u003c\/p\u003e \u003cp\u003e4.5 Conclusion 98\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Virtual Makeup Try-On System Using Cognitive Learning 103\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eDivija Sanapala and J. Angel Arul Jothi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 104\u003c\/p\u003e \u003cp\u003e5.2 Related Works 105\u003c\/p\u003e \u003cp\u003e5.3 Proposed Method 111\u003c\/p\u003e \u003cp\u003e5.4 Experimental Results and Analysis 118\u003c\/p\u003e \u003cp\u003e5.5 Conclusion 119\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Reinforcement Learning for Demand Forecasting and Customized Services 123\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eSini Raj Pulari, T. S. Murugesh, Shriram K. Vasudevan and Akshay Bhuvaneswari Ramakrishnan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 124\u003c\/p\u003e \u003cp\u003e6.2 RL Fundamentals 125\u003c\/p\u003e \u003cp\u003e6.3 Demand Forecasting and Customized Services 130\u003c\/p\u003e \u003cp\u003e6.4 eMart: Forecasting of a Real-World Scenario 131\u003c\/p\u003e \u003cp\u003e6.5 Conclusion and Future Works 133\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 COVID-19 Detection through CT Scan Image Analysis: A Transfer Learning Approach with Ensemble Technique 135\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eP. Padmakumari, S. Vidivelli and P. Shanthi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 136\u003c\/p\u003e \u003cp\u003e7.2 Literature Survey 137\u003c\/p\u003e \u003cp\u003e7.3 Methodology 140\u003c\/p\u003e \u003cp\u003e7.4 Results and Discussion 144\u003c\/p\u003e \u003cp\u003e7.5 Conclusion 148\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Paddy Leaf Classification Using Computational Intelligence 151\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eS. Vidivelli, P. Padmakumari and P. Shanthi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 151\u003c\/p\u003e \u003cp\u003e8.2 Literature Review 153\u003c\/p\u003e \u003cp\u003e8.3 Methodology 155\u003c\/p\u003e \u003cp\u003e8.4 Results and Discussion 160\u003c\/p\u003e \u003cp\u003e8.5 Conclusion 163\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 An Artificial Intelligent Methodology to Classify Knee Joint Disorder Using Machine Learning and Image Processing Techniques 167\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eM. Sharmila Begum, A. V. M. B. Aruna, A. Balajee and R. Murugan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 168\u003c\/p\u003e \u003cp\u003e9.2 Literature Survey 169\u003c\/p\u003e \u003cp\u003e9.3 Proposed Methodology 171\u003c\/p\u003e \u003cp\u003e9.4 Experimental Results 182\u003c\/p\u003e \u003cp\u003e9.5 Conclusion 185\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart III: Advancements in Cognitive Computing: Practical Implementations 189\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Fuzzy-Based Efficient Resource Allocation and Schedulingin a Computational Distributed Environment 191\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eSuguna M., Logesh R. and Om Kumar C. U.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 192\u003c\/p\u003e \u003cp\u003e10.2 Proposed System 193\u003c\/p\u003e \u003cp\u003e10.3 Experimental Results 196\u003c\/p\u003e \u003cp\u003e10.4 Conclusion 201\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 A Lightweight CNN Architecture for Prediction of Plant Diseases 203\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eSasikumar A., Logesh Ravi, Malathi Devarajan, Selvalakshmi A. and Subramaniyaswamy V.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 204\u003c\/p\u003e \u003cp\u003e11.2 Precision Agriculture 206\u003c\/p\u003e \u003cp\u003e11.3 Related Work 211\u003c\/p\u003e \u003cp\u003e11.4 Proposed Architecture for Prediction of Plant Diseases 214\u003c\/p\u003e \u003cp\u003e11.5 Experimental Results and Discussion 217\u003c\/p\u003e \u003cp\u003e11.6 Conclusion 219\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Investigation of Feature Fusioned Dictionary Learning Model for Accurate Brain Tumor Classification 223\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eP. Saravanan, V. Indragandhi, R. Elakkiya and V. Subramaniyaswamy\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 224\u003c\/p\u003e \u003cp\u003e12.2 Literature Review 227\u003c\/p\u003e \u003cp\u003e12.3 Proposed Feature Fusioned Dictionary Learning Model 229\u003c\/p\u003e \u003cp\u003e12.4 Experimental Results and Discussion 232\u003c\/p\u003e \u003cp\u003e12.5 Conclusion and Future Work 235\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Cognitive Analytics-Based Diagnostic Solutions in Healthcare Infrastructure 239\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eAkshay Bhuvaneswari Ramakrishnan, T. S. Murugesh, Sini Raj Pulari and Shriram K. Vasudevan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 240\u003c\/p\u003e \u003cp\u003e13.2 Cognitive Computing in Action 241\u003c\/p\u003e \u003cp\u003e13.3 Increasing the Capabilities of Smart Cities Using Cognitive Computing 243\u003c\/p\u003e \u003cp\u003e13.4 Cognitive Solutions Revolutionizing the Healthcare Industry 246\u003c\/p\u003e \u003cp\u003e13.5 Application of Cognitive Computing to Smart Healthcare in Seoul, South Korea (Case Study) 249\u003c\/p\u003e \u003cp\u003e13.6 Conclusion and Future Work 251\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Automating ESG Score Rating with Reinforcement Learning for Responsible Investment 253\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eMohan Teja G., Logesh Ravi, Malathi Devarajan and Subramaniyaswamy V.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 254\u003c\/p\u003e \u003cp\u003e14.2 Comparative Study 259\u003c\/p\u003e \u003cp\u003e14.3 Literature Survey 263\u003c\/p\u003e \u003cp\u003e14.4 Methods 266\u003c\/p\u003e \u003cp\u003e14.5 Experimental Results 273\u003c\/p\u003e \u003cp\u003e14.6 Discussion 277\u003c\/p\u003e \u003cp\u003e14.7 Conclusion 278\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Reinforcement Learning in Healthcare: Applications and Challenges 283\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eTribhangin Dichpally, Yatish Wutla and Sheela Jayachandran\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction 283\u003c\/p\u003e \u003cp\u003e15.2 Structure of Reinforcement Learning 285\u003c\/p\u003e \u003cp\u003e15.3 Applications 289\u003c\/p\u003e \u003cp\u003e15.4 Challenges 310\u003c\/p\u003e \u003cp\u003e15.5 Conclusion 312\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Cognitive Computing in Smart Cities and Healthcare 317\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eDave Mahadevprasad V., Ondippili Rudhra and Sanjeev Kumar Singh\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e16.1 Introduction 318\u003c\/p\u003e \u003cp\u003e16.2 Machine Learning Inventions and Its Applications 322\u003c\/p\u003e \u003cp\u003e16.3 What is Reinforcement Learning and Cognitive Computing? 326\u003c\/p\u003e \u003cp\u003e16.4 Cognitive Computing 327\u003c\/p\u003e \u003cp\u003e16.5 Data Expressed by the Healthcare and Smart Cities 331\u003c\/p\u003e \u003cp\u003e16.6 Use of Computers to Analyze the Data and Predict the Outcome 332\u003c\/p\u003e \u003cp\u003e16.7 Machine Learning Algorithm 332\u003c\/p\u003e \u003cp\u003e16.8 How to Perform Machine Learning? 336\u003c\/p\u003e \u003cp\u003e16.9 Machine Learning Algorithm 338\u003c\/p\u003e \u003cp\u003e16.10 Common Libraries for Machine Learning Projects 340\u003c\/p\u003e \u003cp\u003e16.11 Supervised Learning Algorithm 341\u003c\/p\u003e \u003cp\u003e16.12 Future of the Healthcare 343\u003c\/p\u003e \u003cp\u003e16.13 Development of Model and Its Workflow 346\u003c\/p\u003e \u003cp\u003e16.13.1 Types of Evaluation 347\u003c\/p\u003e \u003cp\u003e16.14 Future of Smart Cities 347\u003c\/p\u003e \u003cp\u003e16.15 Case Study I 349\u003c\/p\u003e \u003cp\u003e16.16 Case Study II 352\u003c\/p\u003e \u003cp\u003e16.17 Case Study III 355\u003c\/p\u003e \u003cp\u003e16.18 Case Study IV 358\u003c\/p\u003e \u003cp\u003e16.19 Conclusion 360\u003c\/p\u003e \u003cp\u003eReferences 360\u003c\/p\u003e \u003cp\u003eIndex 365\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Electronics \u0026amp; communications engineering [\u003ca title=\"See our other books on Electronics \u0026amp; communications engineering\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Electronics%20\u0026amp;%20communications%20engineering%20%5BTJ%5D%22\"\u003eTJ\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley-Scrivener","offers":[{"title":"Brand New","offer_id":52433207755032,"sku":"9781394214037","price":120.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781394214037.jpg?v=1784851838","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/cognitive-analytics-and-reinforcement-learning-theories-techniques-and-applications-hardback-9781394214037","provider":"Freshly Printed Books","version":"1.0","type":"link"}